CN-122022487-A - Intelligent risk management and control method and system for groundwater pollution

CN122022487ACN 122022487 ACN122022487 ACN 122022487ACN-122022487-A

Abstract

The invention relates to the technical field of risk modeling, in particular to an intelligent risk management and control method and system for groundwater pollution, wherein the method comprises the steps of collecting groundwater quality data and preprocessing the groundwater quality data; extracting pollution state feature vectors reflecting the concentration change and spatial distribution characteristics of pollutants based on the pretreated underground water quality data, and constructing an underground water pollution space-time correlation model, wherein the pollution state feature vectors are compensated by using a weighted space and time adjacent point interpolation method in the process of extracting the pollution state feature vectors to generate compensated pollution state feature vectors, and the current underground water pollution degree is identified by using the compensated pollution state feature vectors and the underground water pollution space-time correlation model. According to the invention, by fusing space-time dynamic modeling and multisource monitoring data intelligent compensation mechanisms, high-precision identification of the groundwater pollution state and reliable prediction of risk trend are realized.

Inventors

Cui Canwen
HUI MENG
ZHANG QIUXIA
NIU XUEKUI
WU XUEYONG
LONG WEI
ZHU FEI
WEI HENG
CHEN YIHUI

Assignees

云南省生态环境科学研究院

Dates

Publication Date: 20260512
Application Date: 20260204

Claims (10)

1. An intelligent risk management and control method for groundwater pollution is characterized by comprising the following steps: s1, collecting underground water quality data and preprocessing the underground water quality data; s2, extracting pollution state feature vectors reflecting the concentration change and spatial distribution features of pollutants based on the pretreated underground water quality data, and constructing an underground water pollution space-time correlation model; the method comprises the steps of extracting a pollution state characteristic vector, wherein the pollution state characteristic vector is compensated by using a weighted space and time adjacent point interpolation method in the process of extracting the pollution state characteristic vector, and the compensated pollution state characteristic vector is generated; S3, identifying the current groundwater pollution degree by using the compensated pollution state feature vector and the groundwater pollution space-time correlation model, and forming a corresponding pollution risk grading evaluation result; s4, based on the pollution risk grading evaluation result, carrying out trend prediction on the underground water pollution risk through a long-term and short-term memory network model, and generating early warning information; S5, generating a matched groundwater pollution risk management and control strategy based on the groundwater pollution risk level, wherein the groundwater pollution risk management and control strategy is used for intelligently managing and controlling groundwater pollution.
2. The method of claim 1, wherein the groundwater quality data includes a concentration of contaminants and an oxidation-reduction potential for characterizing a chemical environmental state of groundwater in S1.
3. The method for intelligent risk management and control of groundwater pollution according to claim 1, wherein in S2, based on the pretreated groundwater quality data, extracting a pollution state feature vector reflecting the concentration change and spatial distribution feature of the pollutant, comprises the following steps: s2.1, aiming at the pretreated underground water quality data, carrying out time sequence alignment on the sampling data of each monitoring well according to a uniform time scale, and mapping the underground water quality data to corresponding space coordinates and aquifer units; s2.2, calculating time change characteristics of a target pollutant concentration sequence at each monitoring well, wherein the time change characteristics comprise a concentration value in unit time and a change rate of the concentration value; S2.3, calculating the spatial gradient strength of the pollutant concentration based on the spatial relationship between adjacent monitoring wells; S2.4, normalizing the time variation characteristics and the spatial gradient strength; and S2.5, splicing the time change characteristics and the spatial gradient strength to form a pollution state characteristic vector for representing the current groundwater pollution state, and compensating the pollution state characteristic vector by using a weighted space and time adjacent point interpolation method.
4. The method for intelligent risk management and control of groundwater pollution according to claim 1, wherein in S2.5, the pollution state feature vector is compensated by using weighted spatial and temporal neighboring interpolation, and the method comprises the following steps: s2.51 for each monitoring point At the time of Checking whether a missing value exists or not, and marking the missing value as target data to be compensated; s2.52, calculating each monitoring point according to the geographical coordinates of the monitoring well With adjacent monitoring points Euclidean distance of (2) Selecting a distance monitoring point Recently, the method of the present invention The monitoring points form a space adjacent set ; S2.53, for each adjacent monitoring point Calculating spatial weights ; S2.54, for each adjacent monitoring point Time stamp of (a) With the target time Calculating time weights ; S2.55, will be adjacent to the monitoring point Is a pollution characteristic value of (2) And carrying out weighted summation according to the spatial weight and the time weight to obtain the compensated pollution state characteristic vector.
5. The method for intelligent risk management and control of groundwater pollution according to claim 1, wherein in S2, a space-time correlation model of groundwater pollution is constructed, and the method comprises the following steps: S2.6, dividing the groundwater pollution monitoring area into a plurality of groundwater space units based on monitoring well distribution, a water-bearing layer structure and hydrogeological boundaries; s2.7, calculating space association weights between any two space units according to the distance between the monitoring wells, the consistency of underground water flow directions and the hydraulic conductivity, constructing a space weight matrix reflecting the migration possibility of pollutants, and performing self-adaptive space-time weight optimization on the space weight matrix to generate a self-adaptive space weight matrix; S2.8, constructing a time sequence model based on the historical pollution state characteristic vector aiming at each underground water space unit; And S2.9, coupling the self-adaptive space weight matrix with the time sequence model, and establishing a space-time correlation model of groundwater pollution, which simultaneously reflects the space diffusion effect and the time evolution characteristic.
6. The method for intelligent risk management and control of groundwater pollution according to claim 1, wherein in S2.7, the adaptive space-time weight optimization is performed on the space weight matrix, and an adaptive space weight matrix is generated, and the method comprises the following steps: s2.71, calculating a sliding window mean value for the pollutant concentration sequence of each monitoring well And standard deviation Identifying abnormal pollution points based on the mean value and the standard deviation; s2.72, mapping the abnormal pollution points to the corresponding groundwater space units by combining the groundwater space unit division results; s2.73, aiming at the underground water space unit and the adjacent underground water space units thereof, introducing an abnormal strength driven weight enhancement mechanism based on a space weight matrix, and adjusting related space association weights.
7. The method for intelligent risk management and control of groundwater pollution according to claim 1, wherein in S3, the current groundwater pollution level is identified by using the compensated pollution state feature vector and the space-time correlation model of groundwater pollution, and a corresponding pollution risk classification evaluation result is formed, and the method comprises the following steps: S3.1, outputting and combining the compensated pollution state characteristic vector with the underground water pollution space-time correlation model to form a comprehensive pollution state matrix of each underground water space unit at the current moment; s3.2, calculating pollution indexes based on the comprehensive pollution state matrix for each groundwater space unit ; S3.3, setting a pollution risk classification threshold according to the underground water functional area standard and the historical pollution data, and mapping the pollution index of each space unit into a risk level; s3.4, mapping the pollution index and the corresponding risk level of each groundwater space unit into a groundwater space grid; S3.5, calculating a comprehensive risk index of the whole monitoring area; S3.6, according to the pollution index and the pollution risk level judgment result of each underground water space unit, and combining the comprehensive risk index, outputting the unit-level underground water pollution risk grading evaluation result and the regional overall pollution risk evaluation result.
8. The method for intelligent risk management and control of groundwater pollution according to claim 1, wherein in S4, based on the result of the classified evaluation of pollution risk, trend prediction is performed on the groundwater pollution risk through a long-short-term memory network model, and early warning information is generated, and the method comprises the following steps: S4.1, receiving the current pollution risk level of each groundwater space unit, and collecting historical pollution state data of the unit; S4.2, arranging the collected historical pollution state data in time sequence to form a risk evolution time sequence of each groundwater space unit; s4.3, extracting key features aiming at the risk evolution time sequence of each unit, and constructing a risk evolution feature vector; s4.4, inputting the risk evolution feature vector into a long-term and short-term memory network model, and predicting pollution risk indexes at future moments; and S4.5, determining the future risk level of each groundwater space unit based on the prediction result of the pollution risk index at the future moment, and generating early warning information.
9. The method for intelligent risk management and control of groundwater pollution according to claim 1, wherein in S5, a matched groundwater pollution risk management and control policy is generated based on the groundwater pollution risk level, and the method comprises the following steps: and comprehensively considering the risk distribution and abnormal concentration degree of adjacent space units, optimizing the intensity and range of management measures, and generating groundwater pollution management and control strategies corresponding to different risk levels.
10. An intelligent risk management and control system for groundwater pollution, comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein execution of the computer program by the processor implements the intelligent risk management and control method for groundwater pollution according to any one of claims 1-9.

Description

Intelligent risk management and control method and system for groundwater pollution Technical Field The invention relates to the technical field of risk modeling, in particular to an intelligent risk management and control method and system for groundwater pollution. Background With the rapid development of industrialization and city, groundwater pollution events are frequent and complicated in cause, and the existing management and control technology faces serious challenges. In the traditional method, a static or quasi-static model is mostly adopted for risk assessment, the dynamic modeling capability of the time evolution and space diffusion process of pollutants in an aquifer is lacked, the complex behaviors such as sudden pollution, non-uniform migration and multi-source coupling are difficult to accurately describe, meanwhile, monitoring data are always lost or abnormal due to uneven sampling, sensor faults or communication delay, so that pollution state representation distortion is caused, and the reliability of risk identification and early warning is further weakened. More importantly, in the current underground water resource management system, the common fracture risk assessment, trend prediction and management and control decision links cannot dynamically adjust the management strategy according to real-time or predicted risks, so that response lag, measure generalization and even resource mismatch are caused. Disclosure of Invention The invention aims to solve the problems in the background technology and provides an intelligent risk management and control method and system for groundwater pollution. The technical scheme of the invention is that the intelligent risk management and control method for groundwater pollution comprises the following steps: s1, collecting underground water quality data and preprocessing the underground water quality data; s2, extracting pollution state feature vectors reflecting the concentration change and spatial distribution features of pollutants based on the pretreated underground water quality data, and constructing an underground water pollution space-time correlation model; the method comprises the steps of extracting a pollution state characteristic vector, wherein the pollution state characteristic vector is compensated by using a weighted space and time adjacent point interpolation method in the process of extracting the pollution state characteristic vector, and the compensated pollution state characteristic vector is generated; S3, identifying the current groundwater pollution degree by using the compensated pollution state feature vector and the groundwater pollution space-time correlation model, and forming a corresponding pollution risk grading evaluation result; s4, based on the pollution risk grading evaluation result, carrying out trend prediction on the underground water pollution risk through a long-term and short-term memory network model, and generating early warning information; S5, generating a matched groundwater pollution risk management and control strategy based on the groundwater pollution risk level, wherein the groundwater pollution risk management and control strategy is used for intelligently managing and controlling groundwater pollution. As a further improvement of the technical scheme, in the step S1, the underground water quality data comprise pollutant concentration and oxidation-reduction potential for representing the chemical environment state of underground water. As a further improvement of the present technical solution, in S2, based on the pretreated underground water quality data, a pollution state feature vector reflecting the concentration variation and the spatial distribution feature of the pollutant is extracted, and the method includes the following steps: s2.1, aiming at the pretreated underground water quality data, carrying out time sequence alignment on the sampling data of each monitoring well according to a uniform time scale, and mapping the underground water quality data to corresponding space coordinates and aquifer units; s2.2, calculating time change characteristics of a target pollutant concentration sequence at each monitoring well, wherein the time change characteristics comprise a concentration value in unit time and a change rate of the concentration value; S2.3, calculating the spatial gradient strength of the pollutant concentration based on the spatial relationship between adjacent monitoring wells; S2.4, normalizing the time variation characteristics and the spatial gradient strength; and S2.5, splicing the time change characteristics and the spatial gradient strength to form a pollution state characteristic vector for representing the current groundwater pollution state, and compensating the pollution state characteristic vector by using a weighted space and time adjacent point interpolation method. As a further improvement of the present technical solution, in S2.5, the method of compensating